This paper presents a simple baseline for few-shot image classification that outperforms state-of-the-art meta-learning methods. The approach involves learning a good embedding model on the meta-training set and then training a linear classifier on top of this embedding. The results show that a good embedding model can be more effective than complex meta-learning algorithms. The authors also demonstrate that self-distillation can further improve performance. The method is evaluated on several benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100, and achieves significant improvements over previous methods. The results suggest that learning a good embedding is more important for few-shot learning than complex meta-learning algorithms. The paper also shows that self-supervised learning can be as effective as supervised learning for few-shot classification. The findings suggest that the role of meta-learning algorithms in few-shot classification may be overstated, and that a good embedding model is sufficient for effective few-shot learning.This paper presents a simple baseline for few-shot image classification that outperforms state-of-the-art meta-learning methods. The approach involves learning a good embedding model on the meta-training set and then training a linear classifier on top of this embedding. The results show that a good embedding model can be more effective than complex meta-learning algorithms. The authors also demonstrate that self-distillation can further improve performance. The method is evaluated on several benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100, and achieves significant improvements over previous methods. The results suggest that learning a good embedding is more important for few-shot learning than complex meta-learning algorithms. The paper also shows that self-supervised learning can be as effective as supervised learning for few-shot classification. The findings suggest that the role of meta-learning algorithms in few-shot classification may be overstated, and that a good embedding model is sufficient for effective few-shot learning.